关键词: 增强学习, 小脑关节模型控制器, 非参数化, 倒立摆

Abstract: In order to solve the problems of high computational complexity and slow convergence rate?of online approximation policy iteration reinforcement learning, this essay proposes a nonparametric approximation policy iteration reinforcement learning?based on CMAC（NPAPI-CMAC）?by introducing CMAC structure as?the value function approximator. The CMAC’s generic parameter is?determined by constructing the sampling process and its state partition mode is confirmed by using initial partition and development partition. The reinforcement learning rate is defined by building sample numbers set of tilling. Through all these ways the reinforcement learning structure and parameters are constructed?completely automatically. In addition, the algorithm uses delta rule and the nearest neighbor method to automatically adjust the parameters of the algorithm in the learning process, and uses?the greedy strategy to select an action?which is obtained from?voting machine. The simulation results on the balancing control of a single inverted pendulum show the effectiveness, robustness and rapid convergence ability of the proposed algorithm.

Key words: reinforcement learning, Cerebellar Model Articulation Controller（CMAC）, nonparametric, inverted pendulum